Optimizing Availability Decomposition for Network Slicing using Bandit Algorithms

Network slices (NSs) are managed through a hierarchical architecture in recent 5G standards. Each NS is formed by connecting autonomously managed network slice subnets (NSSs) across the 5G network domains. To provision a new NS, users specify NS requirements as a network slice request (NSR). The NSR is decomposed into requirements for each NSS, and resources in each domain are allocated. This NSR decomposition is crucial as the selected decomposition affects resource usage and, ultimately, the total number of successfully provisioned NSs. Although several methods address NSR decomposition, most rely either on (i) detailed knowledge of domain-specific resource allocation mechanisms or (ii) extensive historical NS operational data. In practice, however, each domain’s internal processes act as "black boxes" in the hierarchical NS management architecture, making it infeasible to acquire detailed resource allocation algorithms. Additionally, historical data may be insufficient because network slicing remains an emerging technology. These limitations hinder the direct application of existing methods to real operational 5G networks. In this paper, we propose a multi-armed bandits (MABs)-based optimization method that formulates the NSR decomposition as a linear contextual bandits with knapsacks (linCBwK) problem and sequentially acquires the optimal decomposition policy. A MABs-based optimization approach enables us to avoid the need for domain-internal knowledge or extensive pre-collected training data. Simulations demonstrate that our method increases the total number of successfully provisioned NSs by 16.0% compared to the baseline method.